Plotly is an open source, interactive, data visualization tool that interfaces R, Matab, and Python with web based Javascript graphics. Build for collaboration, plotly offers online servers and support with both free and paid subscription plans. In this post I dive in to setting up an online plotly account within RStudio, and I create a variety of visualizations showcasing plotly’s abilities. The visualization examples are inspired by, and in some cases directly copied from the ebook Plotly for R by Carson Sievert. Carson is an interactive data visualization powerhouse, the mastermind behind plotly, and one of my data science heros.

Plotly supports both online and offline functionality. If wish to forgo the tools and collaborative web support provided at https://plot.ly/feed/, simply install the package by following step 1 of the setup guide below. Plotly is still extremely useful without the online tools, this R Markdown document has been created entirely in RStudio. If you want access to the additional web features, follow the entire setup guide.


Setup

  1. Install and load plotly package in R
install.packages('plotly')
library('plotly')

  1. Create an online account - https://plot.ly

  2. Find your credentials on Plot.ly (Settings > API Keys > Regenerate Key)

  3. Set your credentials in R

Sys.setenv("plotly_username"="username-goes-here")
Sys.setenv("plotly_api_key"="api-key-goes-here")
  1. Create a plotly object to test
p <- plot_ly(midwest, x = ~percollege, color = ~state, type = "box")
  1. Push test object to Plot.ly servers
api_create(p, filename = "midwest-boxplots")


It’s that easy! Whenever you want to push an object to Plot.ly use the api_create() function. I highly encourage you at least take a look a the online platform, it’s really fun and easy to use. Here are some of my favorite examples of Plotly in R.


2D Scatterplot

subplot(
  plot_ly(mpg, x = ~cty, y = ~hwy, showlegend=FALSE) %>%
    add_markers(alpha = 0.6,
                size = ~cyl,
                sizes = c(30,50),
                color = ~cyl), 
  plot_ly(mpg, x = ~cty, y = ~hwy) %>%
    add_markers(alpha = 0.6,
                symbol = ~factor(cyl),
                symbols = c(0,1,5,10),
                color = ~factor(cyl)
                )
)


3D Scatterplot

col1 <- colorRamp(c("red", "blue"))

plot_ly(mpg, x = ~cty, y = ~hwy, z = ~cyl) %>%
  add_markers(color = ~cyl,
              colors = col1
              )


Line Plot

library(dplyr)
top5 <- txhousing %>%
  group_by(city) %>%
  summarise(m = mean(sales, na.rm = TRUE)) %>%
  arrange(desc(m)) %>%
  top_n(5)
line_ex <- semi_join(txhousing, top5, by = "city")
col2 <- "Pastel2"

plot_ly(line_ex, x = ~date, y = ~median) %>%
  add_lines(color = ~city,
            colors = col2,
            linetype = ~city
            )


Hex Plot

p <- ggplot(txhousing, aes(date, median)) +
  geom_line(aes(group = city), alpha = 0.2)

subplot(p, 
  ggplot(txhousing, aes(date, median)) + 
    geom_hex(), 
    shareX = TRUE,
    shareY = TRUE
)


Density Plot

kerns <- c("gaussian", "epanechnikov", "rectangular", 
          "triangular", "biweight", "cosine", "optcosine")

p <- plot_ly()

for (k in kerns) {
  d <- density(txhousing$median, kernel = k, na.rm = TRUE)
  p <- add_lines(p, x = d$x, y = d$y, name = k)
}

layout(p, xaxis = list(title = "Median monthly price"))


Histogram

p1 <- plot_ly(diamonds, x = ~price) %>% 
  add_histogram(name = "plotly.js")

price_hist <- function(method = "FD") {
  h <- hist(diamonds$price, breaks = method, plot = FALSE)
  plot_ly(x = h$mids, y = h$counts) %>% 
    add_bars(name = method)
}

subplot(
  p1, 
  price_hist(),
  price_hist("Sturges"),
  price_hist("Scott"),
  nrows = 4, 
  shareX = TRUE
)


Bar Chart

p1 <- plot_ly(diamonds, x = ~cut) %>% 
  add_histogram()

p2 <- diamonds %>%
  dplyr::count(cut) %>%
  plot_ly(x = ~cut, y = ~n, showlegend=FALSE) %>% 
  add_bars()

subplot(p1, p2)


Box Plot

d <- diamonds %>%
  mutate(cc = interaction(clarity, cut))

# interaction levels sorted by median price
lvls <- d %>%
  group_by(cc) %>%
  summarise(m = median(price)) %>%
  arrange(m) %>%
  .[["cc"]]

plot_ly(d, x = ~price, y = ~factor(cc, lvls)) %>%
  add_boxplot(color = ~clarity) %>%
  layout(yaxis = list(title = ""), 
         margin = list(l = 100)
         )


Surface Plot

x <- seq_len(nrow(volcano)) + 100
y <- seq_len(ncol(volcano)) + 500

plot_ly() %>% 
  add_surface(x = ~x, y = ~y, z = ~volcano)


Conclusion

As you can see plotly is a powerful visualization tool that can create superb graphics in just a few lines of code. I haven’t even touched on the amazing spatial visualizations that plotly can make, it’s truely world class. I’m not big into data visualiation but I’m certain that plotly will come in handy many times in the future. Not to mention just how fun it is to use!